Big Data solutions

on AWS


AWS offers a suitable environment to implement Big Data solutions on public Cloud.
It provides several core services, as well as specific for Big Data, that allow this task to be performed reducing time-to-market.

AWS Elastic Map Reduce

Quickly and easily process large volumes of data using frameworks such as Spark, Presto, HBase o Flink.

AWS Lambda

Code-as-a-service execution without the need to configure or manage infrastructure. Pay only for what you use and integrate it with the rest of AWS services.

AWS Kinesis

Like other AWS services, it is fully managed and easily scalable. It collects, processes and analyzes data streams in real time.

AWS Redshift

Database specialized in the storage and processing of large amounts of information, with a higher performance than conventional databases thanks to its capabilities of automatic learning and parallel processing.

AWS Simple Storage Service

Service of objects storage, that due to its characteristics and integration with other AWS services, is used as Data Lake in diverse architectures.

AWS Quicksight

Managed service for displaying data located in different storage systems. No need to administer servers on the scale of 10 to 10,000 users.

Why Amazon Web Services for Big Data projects?

Thanks to the diversity of services offered, AWS becomes an excellent environment to design and implement Big Data projects adapted to business needs. AWS has multiple sources of information that make it easy to make the journey quickly, easily and paying as you go. Examples of this are the definition of reference architectures, technical documents and training courses.

Keepler,
experts on AWS

With 90% of the technical specialists with AWS certifications, including specific Big Data certifications, our architects have been tracking the progress and changes of AWS since its inception, enabling them to have a high level of knowledge and expertise on the platform.

Benefits of AWS cloud solutions

Pay-as-you-go

Separation between compute (EMR) and storage (S3) in Data Lakes, allowing separate cost increases as opposed to provisioning a single cluster.

Scalability

Ability to provision any number of Hadoop clusters of any size as well as the ability to scale out. Also available for Redshift in interactive analysis.

Flexibility

Ability to use Apache Hadoop technologies (Hive, Pig, Spark, Impala…) or SQL oriented languages to launch queries with Redshift and Athena.

High performance

Adjustment of infrastructure to have nodes specialized in computing, memory or network.

Security

Access and execution of data processes using roles for the instances and security groups for network access.

High availability

Data always available thanks to services like S3 or DynamoDB, having an optimum service commitment.

Reliability

Components’ ability to recover from service disruptions, and other features such as reattempts of jobs.

Managed Services

Managed services such as S3, Athena or Quicksight without human operations.

Big Data Architecture in AWS to create Data Products

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AntiPatterns or what you should not do in your Big Data projects

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Let’s talk! 

If you want to make the move to the public AWS cloud, contact us and we’ll talk.